library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.6
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(ggridges)
##
## Attaching package: 'ggridges'
## The following object is masked from 'package:ggplot2':
##
## scale_discrete_manual
library(rnoaa)
weather_df =
rnoaa::meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(id, USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())
weather_df
## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # ... with 1,085 more rows
Blank Plot…
##this is just give you a graph that will show you what it will look like
ggplot(weather_df, aes(x = tmin, y = tmax))
Scatteplot..
##geompoint allows you to take the data and create points out of it
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).
##this plot will give you a plot that focuses only on Central Park
weather_df %>%
filter(name == "CentralPark_NY") %>%
ggplot(aes(x = tmin, y = tmax)) +
geom_point()
## allows you save your plot without using ggsave
weather_sp =
ggplot(weather_df,aes(x = tmin, y = tmax)) +
geom_point()
plot_weather =
weather_df %>%
ggplot(aes(x = tmin, y = tmax))
plot_weather + geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).
Add an aesthetic
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name))
## Warning: Removed 15 rows containing missing values (geom_point).
Add a geom
##geomsmooth() adds a smooth line through your data points, adding se = FALSE turns off the standard error bars which he recommends that we use. Adding an alpha makes the points clearer to see
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = 0.4) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
##Aethetics map is for creating a smooth curve with each color
ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .4) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
Facetting…
## Divide your plot against another vairable, creating separtate plots for each variable
ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .4) +
geom_smooth(se = FALSE) +
facet_grid( ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
A more interesting plot, adding by date
## Boring one...added a geompoint
ggplot(weather_df, aes(x = date, y = tmax)) +
geom_point()
## Warning: Removed 3 rows containing missing values (geom_point).
## Adding color
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_point()
## Warning: Removed 3 rows containing missing values (geom_point).
## Adding a line instead
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_line()
## Smooth, often not enough data to show and would not use
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Hexagon
ggplot(weather_df, aes(x = tmax, y = tmin)) +
geom_hex()
## Warning: Removed 15 rows containing non-finite values (stat_binhex).
## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.
## Allows you to do it by adding precipitation
ggplot(weather_df, aes(x = date, y = tmax, color = name, size = prcp)) +
geom_point() +
geom_smooth(se = FALSE) +
facet_grid(~name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
## Learning Assessment
weather_df %>%
filter(name == "CentralPark_NY") %>%
mutate(tmax_fahr = tmax * (9 / 5) + 32,
tmin_fahr = tmin * (9 / 5) + 32) %>%
ggplot(aes(x = tmin_fahr, y = tmax_fahr)) +
geom_point(alpha = .5) +
geom_smooth(method = "lm", se = FALSE)
## Color is set to be blue outside of the aesthetic
ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin), color = "blue")
## Warning: Removed 15 rows containing missing values (geom_point).
## ggplot is looking for a variable that is blue and will color whichever
ggplot(weather_df) + geom_point(aes(x = tmax, y = tmin, color = "blue"))
## Warning: Removed 15 rows containing missing values (geom_point).
Histograms
##you don't need to identify y because it is a freq, but will need to identify x axis
ggplot(weather_df, aes(x = tmax)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
ggplot(weather_df, aes(x = tmax)) +
geom_histogram() +
facet_grid(~name)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
ggplot(weather_df, aes(x = tmax, fill = name)) +
geom_histogram(position = "dodge", binwidth = 2)
## Warning: Removed 3 rows containing non-finite values (stat_bin).
Density Plot - a smooth histogram
ggplot(weather_df, aes(x = tmax, fill = name)) +
geom_density(alpha = .4, adjust = .5, color = "blue")
## Warning: Removed 3 rows containing non-finite values (stat_density).
Boxplot
##without name
ggplot(weather_df, aes(y = tmax)) +
geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
## with name
ggplot(weather_df, aes(x = name, y = tmax)) +
geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
Violin Plot
## without color
## with color
ggplot(weather_df, aes(x = name, y = tmax)) +
geom_violin(aes(fill = name), color = "blue", alpha = .5) +
stat_summary(fun.y = median, geom = "point", color = "blue", size = 4)
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
## Warning: Removed 3 rows containing non-finite values (stat_summary).
## in class
ggplot(weather_df, aes(x = tmax, y = name)) +
geom_density_ridges()
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).
## Website
ggplot(weather_df, aes(x = tmax, y = name)) +
geom_density_ridges(scale = .85)
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).
weather_plot = ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5)
ggsave("weather_plot.pdf", weather_plot, width = 8, height = 5)
## Warning: Removed 15 rows containing missing values (geom_point).